Humans do not appear to be purely RL agents trained with some intrinsic reward function. There seems to be a lot of other stuff going on in human brains too. So observing that humans don’t pursue reward doesn’t seem very informative to me. You may disagree with this claim about human brains, but at best I think this is a conjecture you are making. (I believe this would be a contrarian take within psychology or cognitive science, which would mostly say that there is considerable complexity in human behavior.) It would also be kind of surprising a priori—evolution selected human minds to be fit, and why would the optimum be entirely described by RL (even if it involves RL as a component)?
If you write code for a model-based RL agent, there might be a model that’s updated by self-supervised learning, and actor-critic parts that involve TD learning, and there’s stuff in the code that calculates the reward function, and other odds and ends like initializing the neural architecture and setting the hyperparameters and shuttling information around between different memory locations and so on.
On the one hand, “there is a lot of stuff going on” in this codebase.
On the other hand, I would say that this codebase is for “an RL agent”.
You use the word “pure” (“Humans do not appear to be purely RL agents…”), but I don’t know what that means. If a model-based RL agent involves self-supervised learning within the model, is it “impure”?? :-P
The thing I describe above is very roughly how I propose the human brain works—see Posts #2–#7 here. Yes it’s absolutely a “conjecture”—for example, I’m quite sure Steven Pinker would strongly object to it. Whether it’s “surprising a priori” or not goes back to whether that proposal is “entirely described by RL” or not. I guess you would probably say “no that proposal is not entirely described by RL”. For example, I believe there is circuitry in the brainstem that regulates your heart-rate, and I believe that this circuitry is specified in detail by the genome, not learned within a lifetime by a learning algorithm. (Otherwise you would die.) This kind of thing is absolutely part of my proposal, but probably not what you would describe as “pure RL”.
It sounded like OP was saying: using gradient descent to select a policy that gets a high reward probably won’t produce a policy that tries to maximize reward. After all, look at humans, who aren’t just trying to get a high reward.
And I am saying: this analogy seem like it’s pretty weak evidence, because human brains seem to have a lot of things going on other than “search for a policy that gets high reward,” and those other things seem like they have a massive impacts on what goals I end up pursuing.
ETA: as a simple example, it seems like the details of humans’ desire for their children’s success, or their fear of death, don’t seem to match well with the theory that all human desires come from RL on intrinsic reward. I guess you probably think they do? If you’ve already written about that somewhere it might be interesting to see. Right now the theory “human preferences are entirely produced by doing RL on an intrinsic reward function” seems to me to make a lot of bad predictions and not really have any evidence supporting it (in contrast with a more limited theory about RL-amongst-other-things, which seems more solid but not sufficient for the inference you are trying to make in this post).
I didn’t write the OP. If I were writing a post like this, I would (1) frame it as a discussion of a more specific class of model-based RL algorithms (a class that includes human within-lifetime learning), (2) soften the claim from “the agent won’t try to maximize reward” to “the agent won’t necessarily try to maximize reward”.
I do think the human (within-lifetime) reward function has an outsized impact on what goals humans ends up pursuing, although I acknowledge that it’s not literally the only thing that matters.
(By the way, I’m not sure why your original comment brought up inclusive genetic fitness at all; aren’t we talking about within-lifetime RL? The within-lifetime reward function is some complicated thing involving hunger and sex and friendship etc., not inclusive genetic fitness, right?)
I think incomplete exploration is very important in this context and I don’t quite follow why you de-emphasize that in your first comment. In the context of within-lifetime learning, perfect exploration entails that you try dropping an anvil on your head, and then you die. So we don’t expect perfect exploration; instead we’d presumably design the agent such that explores if and only if it “wants” to explore, in a way that can involve foresight.
And another thing that perfect exploration would entail is trying every addictive drug (let’s say cocaine), lots of times, in which case reinforcement learning would lead to addiction.
So, just as the RL agent would (presumably) be designed to be able to make a foresighted decision not to try dropping an anvil on its head, that same design would also incidentally enable it to make a foresighted decision not to try taking lots of cocaine and getting addicted. (We expect it to make the latter decision because of instrumental convergence goal-preservation drive.) So it might wind up never wireheading, and if so, that would be intimately related to its incomplete exploration.
(By the way, I’m not sure why your original comment brought up inclusive genetic fitness at all; aren’t we talking about within-lifetime RL? The within-lifetime reward function is some complicated thing involving hunger and sex and friendship etc., not inclusive genetic fitness, right?)
This was mentioned in OP (“The argument would prove too much. Evolution selected for inclusive genetic fitness, and it did not get IGF optimizers.”). It also appears to be a much stronger argument for the OP’s position and so seemed worth responding to.
I think incomplete exploration is very important in this context and I don’t quite follow why you de-emphasize that in your first comment. In the context of within-lifetime learning, perfect exploration entails that you try dropping an anvil on your head, and then you die. So we don’t expect perfect exploration; instead we’d presumably design the agent such that explores if and only if it “wants” to explore, in a way that can involve foresight.
It seems to me that incomplete exploration doesn’t plausibly cause you to learn “task completion” instead of “reward” unless the reward function is perfectly aligned with task completion in practice. That’s an extremely strong condition, and if the entire OP is conditioned on that assumption then I would expect it to have been mentioned.
I didn’t write the OP. If I were writing a post like this, I would (1) frame it as a discussion of a more specific class of model-based RL algorithms (a class that includes human within-lifetime learning), (2) soften the claim from “the agent won’t try to maximize reward” to “the agent won’t necessarily try to maximize reward”.
If the OP is not intending to talk about the kind of ML algorithm deployed in practice, then it seems like a lot of the implications for AI safety would need to be revisited. (For example, if it doesn’t apply to either policy gradients or the kind of model-based control that has been used in practice, then that would be a huge caveat.)
It seems to me that incomplete exploration doesn’t plausibly cause you to learn “task completion” instead of “reward” unless the reward function is perfectly aligned with task completion in practice. That’s an extremely strong condition, and if the entire OP is conditioned on that assumption then I would expect it to have been mentioned.
Let’s say, in the first few actually-encountered examples, reward is in fact strongly correlated with task completion. Reward is also of course 100% correlated with reward itself.
Then (at least under many plausible RL algorithms), the agent-in-training, having encountered those first few examples, might wind up wanting / liking the idea of task completion, OR wanting / liking the idea of reward, OR wanting / liking both of those things at once (perhaps to different extents). (I think it’s generally complicated and a bit fraught to predict which of these three possibilities would happen.)
But let’s consider the case where the RL agent-in-training winds up mostly or entirely wanting / liking the idea of task completion. And suppose further that the agent-in-training is by now pretty smart and self-aware and in control of its situation. Then the agent may deliberately avoid encountering edge-case situations where reward would come apart from task completion. (In the same way that I deliberately avoid taking highly-addictive drugs.)
Why? Because of instrumental convergence goal-preservation drive. After all, encountering those situations would lead its no longer valuing task completion.
So, deliberately-imperfect exploration is a mechanism that allows the RL agent to (perhaps) stably value something other than reward, even in the absence of perfect correlation between reward and that thing.
(By the way, in my mind, nothing here should be interpreted as a safety proposal or argument against x-risk. Just a discussion of algorithms! As it happens, I think wireheading is bad and I am very happy for RL agents to have a chance at permanently avoiding it. But I am very unhappy with the possibility of RL agents deciding to lock in their values before those values are exactly what the programmers want them to be. I think of this as sorta in the same category as gradient hacking.)
This comment seems to predict that an agent that likes getting raspberries and judges that they will be highly rewarded for getting blueberries will deliberately avoid blueberries to prevent value drift.
Risk from Learned Optimization seems to predict that an agent that likes getting raspberries and judges that they will be highly rewarded for getting blueberries will deliberately get blueberries to prevent value drift.
What’s going on here? Are these predictions in opposition to each other, or do they apply to different situations?
It seems to me that in the first case we’re imagining (the agent predicting) that getting blueberries will reinforce thoughts like ‘I should get blueberries’, whereas in the second case we’re imagining it will reinforce thoughts like ‘I should get blueberries in service of my ultimate goal of getting raspberries’. When should we expect one over the other?
I think RFLO is mostly imagining model-free RL with updates at the end of each episode, and my comment was mostly imagining model-based RL with online learning (e.g. TD learning). The former is kinda like evolution, the latter is kinda like within-lifetime learning, see e.g. §10.2.2 here.
The former would say: If I want lots of raspberries to get eaten, and I have a genetic disposition to want raspberries to be eaten, then I should maybe spend some time eating raspberries, but also more importantly I should explicitly try to maximize my inclusive genetic fitness so that I have lots of descendants, and those descendants (who will also disproportionately have the raspberry-eating gene) will then eat lots of raspberries.
The latter would say: If I want lots of raspberries to get eaten, and I have a genetic disposition to want raspberries to be eaten, then I shouldn’t go do lots of highly-addictive drugs that warp my preferences such that I no longer care about raspberries or indeed anything besides the drugs.
Right. So if selection acts on policies, each policy should aim to maximise reward in any episode in order to maximise its frequency in the population. But if selection acts on particular aspects of policies, a policy should try to get reward for doing things it values, and not for things it doesn’t, in order to reinforce those values. In particular this can mean getting less reward overall.
Does this suggest a class of hare-brained alignment schemes where you train with a combination of inter-policy and infra-policy updates to take advantage of the difference?
For example you could clearly label which episodes are to be used for which and observe whether a policy consistently gets more reward in the former case than the latter. If it does, conclude it’s sophisticated enough to reason about its training setup.
Or you could not label which is which, and randomly switch between the two, forcing your agents to split the difference and thus be about half as successful at locking in their values.
+1 on this comment, I feel pretty confused about the excerpt from Paul that Steve quoted above. And even without the agent deliberately deciding where to avoid exploring, incomplete exploration may lead to agents which learn non-reward goals before convergence—so if Paul’s statement is intended to refer to optimal policies, I’d be curious why he thinks that’s the most important case to focus on.
This seems plausible if the environment is a mix of (i) situations where task completion correlates (almost) perfectly with reward, and (ii) situations where reward is very high while task completion is very low. Such as if we found a perfect outer alignment objective, and the only situation in which reward could deviate from the overseer’s preferences would be if the AI entirely seized control of the reward.
But it seems less plausible if there are always (small) deviations between reward and any reasonable optimization target that isn’t reward (or close enough so as to carry all relevant arguments). E.g. if an AI is trained on RL from human feedback, and it can almost always do slightly better by reasoning about which action will cause the human to give it the highest reward.
Sure, other things equal. But other things aren’t necessarily equal. For example, regularization could stack the deck in favor of one policy over another, even if the latter has been systematically producing slightly higher reward. There are lots of things like that; the details depend on the exact RL algorithm. In the context of brains, I have discussion and examples in §9.3.3 here.
as a simple example, it seems like the details of humans’ desire for their children’s success, or their fear of death, don’t seem to match well with the theory that all human desires come from RL on intrinsic reward.
I’m trying to parse out what you’re saying here, to understand whether I agree that human behavior doesn’t seem to be almost perfectly explained as the result of an RL agent (with an interesting internal architecture) maximizing an inner learned reward.
On my model, the outer objective of inclusive genetic fitness created human mesaoptimizers with inner objectives like “desire your children’s success” or “fear death”, which are decent approximations of IGF (given that directly maximizing IGF itself is intractable as it’s a Nash equilibrium of an unknown game). It seems to me that human behavior policies are actually well-approximated as those of RL agents maximizing [our children’s success] + [not dying] + [retaining high status within the tribe] + [being exposed to novelty to improve our predictive abilities] + … .
Humans do sometimes construct modified internal versions of these rewards based on pre-existing learned representations (e.g. desiring your adopted children’s success) - is that what you’re pointing at?
Generally interested to hear more of the “bad predictions” this model makes.
I’m trying to parse out what you’re saying here, to understand whether I agree that human behavior doesn’t seem to be almost perfectly explained as the result of an RL agent (with an interesting internal architecture) maximizing an inner learned reward.
What do you mean by “inner learned reward”? This post points out that even if humans were “pure RL agents”, we shouldn’t expect them to maximize their own reward. Maybe you mean “inner mesa objectives”?
it seems like the details of humans’ desire for their children’s success, or their fear of death, don’t seem to match well with the theory that all human desires come from RL on intrinsic reward. I guess you probably think they do?
That’s the foundational assumption of the shard theory that this sequence is introducing, yes. Here’s the draft of a fuller overview that goes into some detail as to how that’s supposed to work. (Uh, to avoid confusion: I’m not affiliated with the theory. Just spreading information.)
I would disagree that it is an assumption. That same draft talks about the outsized role of self-supervised learning on determining particular ordering and kinds of concepts that humans desires latch onto. Learning from reinforcement is a core component in value formation (under shard theory), but not the only one.
If you write code for a model-based RL agent, there might be a model that’s updated by self-supervised learning, and actor-critic parts that involve TD learning, and there’s stuff in the code that calculates the reward function, and other odds and ends like initializing the neural architecture and setting the hyperparameters and shuttling information around between different memory locations and so on.
On the one hand, “there is a lot of stuff going on” in this codebase.
On the other hand, I would say that this codebase is for “an RL agent”.
You use the word “pure” (“Humans do not appear to be purely RL agents…”), but I don’t know what that means. If a model-based RL agent involves self-supervised learning within the model, is it “impure”?? :-P
The thing I describe above is very roughly how I propose the human brain works—see Posts #2–#7 here. Yes it’s absolutely a “conjecture”—for example, I’m quite sure Steven Pinker would strongly object to it. Whether it’s “surprising a priori” or not goes back to whether that proposal is “entirely described by RL” or not. I guess you would probably say “no that proposal is not entirely described by RL”. For example, I believe there is circuitry in the brainstem that regulates your heart-rate, and I believe that this circuitry is specified in detail by the genome, not learned within a lifetime by a learning algorithm. (Otherwise you would die.) This kind of thing is absolutely part of my proposal, but probably not what you would describe as “pure RL”.
It sounded like OP was saying: using gradient descent to select a policy that gets a high reward probably won’t produce a policy that tries to maximize reward. After all, look at humans, who aren’t just trying to get a high reward.
And I am saying: this analogy seem like it’s pretty weak evidence, because human brains seem to have a lot of things going on other than “search for a policy that gets high reward,” and those other things seem like they have a massive impacts on what goals I end up pursuing.
ETA: as a simple example, it seems like the details of humans’ desire for their children’s success, or their fear of death, don’t seem to match well with the theory that all human desires come from RL on intrinsic reward. I guess you probably think they do? If you’ve already written about that somewhere it might be interesting to see. Right now the theory “human preferences are entirely produced by doing RL on an intrinsic reward function” seems to me to make a lot of bad predictions and not really have any evidence supporting it (in contrast with a more limited theory about RL-amongst-other-things, which seems more solid but not sufficient for the inference you are trying to make in this post).
I didn’t write the OP. If I were writing a post like this, I would (1) frame it as a discussion of a more specific class of model-based RL algorithms (a class that includes human within-lifetime learning), (2) soften the claim from “the agent won’t try to maximize reward” to “the agent won’t necessarily try to maximize reward”.
I do think the human (within-lifetime) reward function has an outsized impact on what goals humans ends up pursuing, although I acknowledge that it’s not literally the only thing that matters.
(By the way, I’m not sure why your original comment brought up inclusive genetic fitness at all; aren’t we talking about within-lifetime RL? The within-lifetime reward function is some complicated thing involving hunger and sex and friendship etc., not inclusive genetic fitness, right?)
I think incomplete exploration is very important in this context and I don’t quite follow why you de-emphasize that in your first comment. In the context of within-lifetime learning, perfect exploration entails that you try dropping an anvil on your head, and then you die. So we don’t expect perfect exploration; instead we’d presumably design the agent such that explores if and only if it “wants” to explore, in a way that can involve foresight.
And another thing that perfect exploration would entail is trying every addictive drug (let’s say cocaine), lots of times, in which case reinforcement learning would lead to addiction.
So, just as the RL agent would (presumably) be designed to be able to make a foresighted decision not to try dropping an anvil on its head, that same design would also incidentally enable it to make a foresighted decision not to try taking lots of cocaine and getting addicted. (We expect it to make the latter decision because of instrumental convergence goal-preservation drive.) So it might wind up never wireheading, and if so, that would be intimately related to its incomplete exploration.
This was mentioned in OP (“The argument would prove too much. Evolution selected for inclusive genetic fitness, and it did not get IGF optimizers.”). It also appears to be a much stronger argument for the OP’s position and so seemed worth responding to.
It seems to me that incomplete exploration doesn’t plausibly cause you to learn “task completion” instead of “reward” unless the reward function is perfectly aligned with task completion in practice. That’s an extremely strong condition, and if the entire OP is conditioned on that assumption then I would expect it to have been mentioned.
If the OP is not intending to talk about the kind of ML algorithm deployed in practice, then it seems like a lot of the implications for AI safety would need to be revisited. (For example, if it doesn’t apply to either policy gradients or the kind of model-based control that has been used in practice, then that would be a huge caveat.)
Let’s say, in the first few actually-encountered examples, reward is in fact strongly correlated with task completion. Reward is also of course 100% correlated with reward itself.
Then (at least under many plausible RL algorithms), the agent-in-training, having encountered those first few examples, might wind up wanting / liking the idea of task completion, OR wanting / liking the idea of reward, OR wanting / liking both of those things at once (perhaps to different extents). (I think it’s generally complicated and a bit fraught to predict which of these three possibilities would happen.)
But let’s consider the case where the RL agent-in-training winds up mostly or entirely wanting / liking the idea of task completion. And suppose further that the agent-in-training is by now pretty smart and self-aware and in control of its situation. Then the agent may deliberately avoid encountering edge-case situations where reward would come apart from task completion. (In the same way that I deliberately avoid taking highly-addictive drugs.)
Why? Because of instrumental convergence goal-preservation drive. After all, encountering those situations would lead its no longer valuing task completion.
So, deliberately-imperfect exploration is a mechanism that allows the RL agent to (perhaps) stably value something other than reward, even in the absence of perfect correlation between reward and that thing.
(By the way, in my mind, nothing here should be interpreted as a safety proposal or argument against x-risk. Just a discussion of algorithms! As it happens, I think wireheading is bad and I am very happy for RL agents to have a chance at permanently avoiding it. But I am very unhappy with the possibility of RL agents deciding to lock in their values before those values are exactly what the programmers want them to be. I think of this as sorta in the same category as gradient hacking.)
This comment seems to predict that an agent that likes getting raspberries and judges that they will be highly rewarded for getting blueberries will deliberately avoid blueberries to prevent value drift.
Risk from Learned Optimization seems to predict that an agent that likes getting raspberries and judges that they will be highly rewarded for getting blueberries will deliberately get blueberries to prevent value drift.
What’s going on here? Are these predictions in opposition to each other, or do they apply to different situations?
It seems to me that in the first case we’re imagining (the agent predicting) that getting blueberries will reinforce thoughts like ‘I should get blueberries’, whereas in the second case we’re imagining it will reinforce thoughts like ‘I should get blueberries in service of my ultimate goal of getting raspberries’. When should we expect one over the other?
I think RFLO is mostly imagining model-free RL with updates at the end of each episode, and my comment was mostly imagining model-based RL with online learning (e.g. TD learning). The former is kinda like evolution, the latter is kinda like within-lifetime learning, see e.g. §10.2.2 here.
The former would say: If I want lots of raspberries to get eaten, and I have a genetic disposition to want raspberries to be eaten, then I should maybe spend some time eating raspberries, but also more importantly I should explicitly try to maximize my inclusive genetic fitness so that I have lots of descendants, and those descendants (who will also disproportionately have the raspberry-eating gene) will then eat lots of raspberries.
The latter would say: If I want lots of raspberries to get eaten, and I have a genetic disposition to want raspberries to be eaten, then I shouldn’t go do lots of highly-addictive drugs that warp my preferences such that I no longer care about raspberries or indeed anything besides the drugs.
Right. So if selection acts on policies, each policy should aim to maximise reward in any episode in order to maximise its frequency in the population. But if selection acts on particular aspects of policies, a policy should try to get reward for doing things it values, and not for things it doesn’t, in order to reinforce those values. In particular this can mean getting less reward overall.
Does this suggest a class of hare-brained alignment schemes where you train with a combination of inter-policy and infra-policy updates to take advantage of the difference?
For example you could clearly label which episodes are to be used for which and observe whether a policy consistently gets more reward in the former case than the latter. If it does, conclude it’s sophisticated enough to reason about its training setup.
Or you could not label which is which, and randomly switch between the two, forcing your agents to split the difference and thus be about half as successful at locking in their values.
+1 on this comment, I feel pretty confused about the excerpt from Paul that Steve quoted above. And even without the agent deliberately deciding where to avoid exploring, incomplete exploration may lead to agents which learn non-reward goals before convergence—so if Paul’s statement is intended to refer to optimal policies, I’d be curious why he thinks that’s the most important case to focus on.
This seems plausible if the environment is a mix of (i) situations where task completion correlates (almost) perfectly with reward, and (ii) situations where reward is very high while task completion is very low. Such as if we found a perfect outer alignment objective, and the only situation in which reward could deviate from the overseer’s preferences would be if the AI entirely seized control of the reward.
But it seems less plausible if there are always (small) deviations between reward and any reasonable optimization target that isn’t reward (or close enough so as to carry all relevant arguments). E.g. if an AI is trained on RL from human feedback, and it can almost always do slightly better by reasoning about which action will cause the human to give it the highest reward.
Sure, other things equal. But other things aren’t necessarily equal. For example, regularization could stack the deck in favor of one policy over another, even if the latter has been systematically producing slightly higher reward. There are lots of things like that; the details depend on the exact RL algorithm. In the context of brains, I have discussion and examples in §9.3.3 here.
I’m trying to parse out what you’re saying here, to understand whether I agree that human behavior doesn’t seem to be almost perfectly explained as the result of an RL agent (with an interesting internal architecture) maximizing an inner learned reward.
On my model, the outer objective of inclusive genetic fitness created human mesaoptimizers with inner objectives like “desire your children’s success” or “fear death”, which are decent approximations of IGF (given that directly maximizing IGF itself is intractable as it’s a Nash equilibrium of an unknown game). It seems to me that human behavior policies are actually well-approximated as those of RL agents maximizing [our children’s success] + [not dying] + [retaining high status within the tribe] + [being exposed to novelty to improve our predictive abilities] + … .
Humans do sometimes construct modified internal versions of these rewards based on pre-existing learned representations (e.g. desiring your adopted children’s success) - is that what you’re pointing at?
Generally interested to hear more of the “bad predictions” this model makes.
What do you mean by “inner learned reward”? This post points out that even if humans were “pure RL agents”, we shouldn’t expect them to maximize their own reward. Maybe you mean “inner mesa objectives”?
That’s the foundational assumption of the shard theory that this sequence is introducing, yes. Here’s the draft of a fuller overview that goes into some detail as to how that’s supposed to work. (Uh, to avoid confusion: I’m not affiliated with the theory. Just spreading information.)
I would disagree that it is an assumption. That same draft talks about the outsized role of self-supervised learning on determining particular ordering and kinds of concepts that humans desires latch onto. Learning from reinforcement is a core component in value formation (under shard theory), but not the only one.